Overview

Dataset statistics

Number of variables44
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory79.3 KiB
Average record size in memory1.6 KiB

Variable types

Categorical21
Numeric18
Boolean5

Alerts

Age is highly overall correlated with Customer_IDHigh correlation
Residence_Years is highly overall correlated with Customer_IDHigh correlation
Monthly_Income is highly overall correlated with Spending_to_Income_Ratio and 3 other fieldsHigh correlation
Experience_Years is highly overall correlated with Customer_IDHigh correlation
Monthly_Spending is highly overall correlated with Spending_to_Income_Ratio and 1 other fieldsHigh correlation
Savings_Balance is highly overall correlated with Customer_IDHigh correlation
Credit_Amount is highly overall correlated with Loan_to_Income_Ratio and 1 other fieldsHigh correlation
Installment_Rate is highly overall correlated with Customer_IDHigh correlation
Account_Tenure_Months is highly overall correlated with Customer_IDHigh correlation
Monthly_Transactions_Count is highly overall correlated with Customer_ID and 1 other fieldsHigh correlation
Avg_Transaction_Value is highly overall correlated with Customer_IDHigh correlation
Number_Credit_Inquiries_6mo is highly overall correlated with Customer_IDHigh correlation
Credit_Limit is highly overall correlated with Credit_Utilization_Ratio and 2 other fieldsHigh correlation
Revolving_Balance is highly overall correlated with Credit_Utilization_Ratio and 1 other fieldsHigh correlation
Spending_to_Income_Ratio is highly overall correlated with Monthly_Income and 2 other fieldsHigh correlation
Loan_to_Income_Ratio is highly overall correlated with Monthly_Income and 3 other fieldsHigh correlation
Credit_Utilization_Ratio is highly overall correlated with Credit_Limit and 2 other fieldsHigh correlation
Behavioral_Score is highly overall correlated with Customer_IDHigh correlation
Customer_ID is highly overall correlated with Age and 42 other fieldsHigh correlation
Gender is highly overall correlated with Credit_Limit and 1 other fieldsHigh correlation
Marital_Status is highly overall correlated with Monthly_Transactions_Count and 1 other fieldsHigh correlation
Dependents is highly overall correlated with Customer_IDHigh correlation
Education_Level is highly overall correlated with Customer_IDHigh correlation
Employment_Status is highly overall correlated with Customer_IDHigh correlation
Job_Type is highly overall correlated with Customer_IDHigh correlation
Loan_Purpose is highly overall correlated with Customer_IDHigh correlation
Credit_Card_Usage is highly overall correlated with Customer_IDHigh correlation
Payment_History is highly overall correlated with Customer_IDHigh correlation
Other_Loans is highly overall correlated with Customer_IDHigh correlation
Default_History is highly overall correlated with Customer_IDHigh correlation
Checking_Account_Status is highly overall correlated with Customer_IDHigh correlation
Savings_Account_Status is highly overall correlated with Customer_IDHigh correlation
Property is highly overall correlated with Customer_IDHigh correlation
Housing is highly overall correlated with Customer_IDHigh correlation
Foreign_Worker is highly overall correlated with Customer_IDHigh correlation
Mobile_Banking_Active is highly overall correlated with Customer_IDHigh correlation
Auto_Pay_Enabled is highly overall correlated with Customer_IDHigh correlation
Num_Products_With_Bank is highly overall correlated with Customer_IDHigh correlation
Insurance_Coverage is highly overall correlated with Customer_IDHigh correlation
Financial_Literacy_Score is highly overall correlated with Customer_IDHigh correlation
Region is highly overall correlated with Customer_IDHigh correlation
Employment_Industry is highly overall correlated with Customer_IDHigh correlation
Days_Since_Last_Default is highly overall correlated with Customer_IDHigh correlation
Credit_Risk is highly overall correlated with Monthly_Income and 2 other fieldsHigh correlation
Default_History is highly imbalanced (59.8%)Imbalance
Days_Since_Last_Default is highly imbalanced (52.2%)Imbalance
Customer_ID is uniformly distributedUniform
Customer_ID has unique valuesUnique
Monthly_Income has unique valuesUnique
Monthly_Spending has unique valuesUnique
Savings_Balance has unique valuesUnique
Credit_Amount has unique valuesUnique
Installment_Rate has unique valuesUnique
Avg_Transaction_Value has unique valuesUnique
Credit_Limit has unique valuesUnique
Revolving_Balance has unique valuesUnique
Loan_to_Income_Ratio has unique valuesUnique
Experience_Years has 1 (2.0%) zerosZeros
Number_Credit_Inquiries_6mo has 7 (14.0%) zerosZeros

Reproduction

Analysis started2025-10-11 16:34:01.133871
Analysis finished2025-10-11 16:35:01.433522
Duration1 minute and 0.3 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

Customer_ID
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
C001
 
1
C038
 
1
C028
 
1
C029
 
1
C030
 
1
Other values (45)
45 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters200
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowC001
2nd rowC002
3rd rowC003
4th rowC004
5th rowC005

Common Values

ValueCountFrequency (%)
C001 1
 
2.0%
C038 1
 
2.0%
C028 1
 
2.0%
C029 1
 
2.0%
C030 1
 
2.0%
C031 1
 
2.0%
C032 1
 
2.0%
C033 1
 
2.0%
C034 1
 
2.0%
C035 1
 
2.0%
Other values (40) 40
80.0%

Length

2025-10-11T21:35:01.558743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c001 1
 
2.0%
c012 1
 
2.0%
c024 1
 
2.0%
c003 1
 
2.0%
c004 1
 
2.0%
c005 1
 
2.0%
c006 1
 
2.0%
c007 1
 
2.0%
c008 1
 
2.0%
c009 1
 
2.0%
Other values (40) 40
80.0%

Most occurring characters

ValueCountFrequency (%)
0 64
32.0%
C 50
25.0%
1 15
 
7.5%
3 15
 
7.5%
2 15
 
7.5%
4 15
 
7.5%
5 6
 
3.0%
8 5
 
2.5%
9 5
 
2.5%
6 5
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 150
75.0%
Uppercase Letter 50
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 64
42.7%
1 15
 
10.0%
3 15
 
10.0%
2 15
 
10.0%
4 15
 
10.0%
5 6
 
4.0%
8 5
 
3.3%
9 5
 
3.3%
6 5
 
3.3%
7 5
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
C 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 150
75.0%
Latin 50
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 64
42.7%
1 15
 
10.0%
3 15
 
10.0%
2 15
 
10.0%
4 15
 
10.0%
5 6
 
4.0%
8 5
 
3.3%
9 5
 
3.3%
6 5
 
3.3%
7 5
 
3.3%
Latin
ValueCountFrequency (%)
C 50
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 64
32.0%
C 50
25.0%
1 15
 
7.5%
3 15
 
7.5%
2 15
 
7.5%
4 15
 
7.5%
5 6
 
3.0%
8 5
 
2.5%
9 5
 
2.5%
6 5
 
2.5%

Age
Real number (ℝ)

Distinct33
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.76
Minimum22
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:01.710791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile22.45
Q132.5
median42.5
Q352.25
95-th percentile63.55
Maximum64
Range42
Interquartile range (IQR)19.75

Descriptive statistics

Standard deviation12.846917
Coefficient of variation (CV)0.30044239
Kurtosis-0.99944326
Mean42.76
Median Absolute Deviation (MAD)10.5
Skewness0.051889726
Sum2138
Variance165.04327
MonotonicityNot monotonic
2025-10-11T21:35:01.840972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
59 3
 
6.0%
44 3
 
6.0%
64 3
 
6.0%
22 3
 
6.0%
41 3
 
6.0%
48 2
 
4.0%
42 2
 
4.0%
23 2
 
4.0%
29 2
 
4.0%
31 2
 
4.0%
Other values (23) 25
50.0%
ValueCountFrequency (%)
22 3
6.0%
23 2
4.0%
24 1
 
2.0%
27 1
 
2.0%
28 1
 
2.0%
29 2
4.0%
31 2
4.0%
32 1
 
2.0%
34 1
 
2.0%
35 2
4.0%
ValueCountFrequency (%)
64 3
6.0%
63 1
 
2.0%
62 1
 
2.0%
60 1
 
2.0%
59 3
6.0%
58 1
 
2.0%
57 1
 
2.0%
56 1
 
2.0%
53 1
 
2.0%
50 1
 
2.0%

Gender
Categorical

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Female
33 
Male
17 

Length

Max length6
Median length6
Mean length5.32
Min length4

Characters and Unicode

Total characters266
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 33
66.0%
Male 17
34.0%

Length

2025-10-11T21:35:01.983051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:02.140935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
female 33
66.0%
male 17
34.0%

Most occurring characters

ValueCountFrequency (%)
e 83
31.2%
a 50
18.8%
l 50
18.8%
F 33
 
12.4%
m 33
 
12.4%
M 17
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 216
81.2%
Uppercase Letter 50
 
18.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 83
38.4%
a 50
23.1%
l 50
23.1%
m 33
 
15.3%
Uppercase Letter
ValueCountFrequency (%)
F 33
66.0%
M 17
34.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 266
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 83
31.2%
a 50
18.8%
l 50
18.8%
F 33
 
12.4%
m 33
 
12.4%
M 17
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 83
31.2%
a 50
18.8%
l 50
18.8%
F 33
 
12.4%
m 33
 
12.4%
M 17
 
6.4%

Marital_Status
Categorical

Distinct4
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Married
23 
Single
22 
Divorced
Widowed
 
1

Length

Max length8
Median length7
Mean length6.64
Min length6

Characters and Unicode

Total characters332
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st rowSingle
2nd rowMarried
3rd rowSingle
4th rowMarried
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 23
46.0%
Single 22
44.0%
Divorced 4
 
8.0%
Widowed 1
 
2.0%

Length

2025-10-11T21:35:02.283340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:02.500830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
married 23
46.0%
single 22
44.0%
divorced 4
 
8.0%
widowed 1
 
2.0%

Most occurring characters

ValueCountFrequency (%)
r 50
15.1%
i 50
15.1%
e 50
15.1%
d 29
8.7%
M 23
6.9%
a 23
6.9%
S 22
6.6%
n 22
6.6%
g 22
6.6%
l 22
6.6%
Other values (6) 19
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 282
84.9%
Uppercase Letter 50
 
15.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 50
17.7%
i 50
17.7%
e 50
17.7%
d 29
10.3%
a 23
8.2%
n 22
7.8%
g 22
7.8%
l 22
7.8%
o 5
 
1.8%
v 4
 
1.4%
Other values (2) 5
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
M 23
46.0%
S 22
44.0%
D 4
 
8.0%
W 1
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 332
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 50
15.1%
i 50
15.1%
e 50
15.1%
d 29
8.7%
M 23
6.9%
a 23
6.9%
S 22
6.6%
n 22
6.6%
g 22
6.6%
l 22
6.6%
Other values (6) 19
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 50
15.1%
i 50
15.1%
e 50
15.1%
d 29
8.7%
M 23
6.9%
a 23
6.9%
S 22
6.6%
n 22
6.6%
g 22
6.6%
l 22
6.6%
Other values (6) 19
 
5.7%

Dependents
Categorical

Distinct5
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0
18 
3
12 
2
1
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
0 18
36.0%
3 12
24.0%
2 9
18.0%
1 6
 
12.0%
4 5
 
10.0%

Length

2025-10-11T21:35:02.669404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:02.860575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18
36.0%
3 12
24.0%
2 9
18.0%
1 6
 
12.0%
4 5
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 18
36.0%
3 12
24.0%
2 9
18.0%
1 6
 
12.0%
4 5
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18
36.0%
3 12
24.0%
2 9
18.0%
1 6
 
12.0%
4 5
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18
36.0%
3 12
24.0%
2 9
18.0%
1 6
 
12.0%
4 5
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18
36.0%
3 12
24.0%
2 9
18.0%
1 6
 
12.0%
4 5
 
10.0%

Residence_Years
Real number (ℝ)

Distinct14
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.86
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:03.022280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum14
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6922976
Coefficient of variation (CV)0.53823579
Kurtosis-1.1367069
Mean6.86
Median Absolute Deviation (MAD)3
Skewness0.057311316
Sum343
Variance13.633061
MonotonicityNot monotonic
2025-10-11T21:35:03.195581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
4 6
12.0%
11 6
12.0%
6 5
10.0%
12 4
8.0%
7 4
8.0%
8 4
8.0%
1 4
8.0%
9 4
8.0%
3 4
8.0%
2 3
6.0%
Other values (4) 6
12.0%
ValueCountFrequency (%)
1 4
8.0%
2 3
6.0%
3 4
8.0%
4 6
12.0%
5 2
 
4.0%
6 5
10.0%
7 4
8.0%
8 4
8.0%
9 4
8.0%
10 2
 
4.0%
ValueCountFrequency (%)
14 1
 
2.0%
13 1
 
2.0%
12 4
8.0%
11 6
12.0%
10 2
 
4.0%
9 4
8.0%
8 4
8.0%
7 4
8.0%
6 5
10.0%
5 2
 
4.0%

Education_Level
Categorical

Distinct4
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
Graduate
21 
Postgraduate
12 
High School
11 
PhD

Length

Max length12
Median length11
Mean length9.02
Min length3

Characters and Unicode

Total characters451
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh School
2nd rowGraduate
3rd rowGraduate
4th rowHigh School
5th rowHigh School

Common Values

ValueCountFrequency (%)
Graduate 21
42.0%
Postgraduate 12
24.0%
High School 11
22.0%
PhD 6
 
12.0%

Length

2025-10-11T21:35:03.377273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:03.547909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
graduate 21
34.4%
postgraduate 12
19.7%
high 11
18.0%
school 11
18.0%
phd 6
 
9.8%

Most occurring characters

ValueCountFrequency (%)
a 66
14.6%
t 45
10.0%
o 34
 
7.5%
d 33
 
7.3%
u 33
 
7.3%
e 33
 
7.3%
r 33
 
7.3%
h 28
 
6.2%
g 23
 
5.1%
G 21
 
4.7%
Other values (9) 102
22.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 373
82.7%
Uppercase Letter 67
 
14.9%
Space Separator 11
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 66
17.7%
t 45
12.1%
o 34
9.1%
d 33
8.8%
u 33
8.8%
e 33
8.8%
r 33
8.8%
h 28
7.5%
g 23
 
6.2%
s 12
 
3.2%
Other values (3) 33
8.8%
Uppercase Letter
ValueCountFrequency (%)
G 21
31.3%
P 18
26.9%
H 11
16.4%
S 11
16.4%
D 6
 
9.0%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 440
97.6%
Common 11
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 66
15.0%
t 45
10.2%
o 34
 
7.7%
d 33
 
7.5%
u 33
 
7.5%
e 33
 
7.5%
r 33
 
7.5%
h 28
 
6.4%
g 23
 
5.2%
G 21
 
4.8%
Other values (8) 91
20.7%
Common
ValueCountFrequency (%)
11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 451
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 66
14.6%
t 45
10.0%
o 34
 
7.5%
d 33
 
7.3%
u 33
 
7.3%
e 33
 
7.3%
r 33
 
7.3%
h 28
 
6.2%
g 23
 
5.1%
G 21
 
4.7%
Other values (9) 102
22.6%
Distinct4
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
Salaried
38 
Self-employed
Retired
 
3
Unemployed
 
2

Length

Max length13
Median length8
Mean length8.72
Min length7

Characters and Unicode

Total characters436
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSalaried
2nd rowSalaried
3rd rowSelf-employed
4th rowSalaried
5th rowSalaried

Common Values

ValueCountFrequency (%)
Salaried 38
76.0%
Self-employed 7
 
14.0%
Retired 3
 
6.0%
Unemployed 2
 
4.0%

Length

2025-10-11T21:35:03.717993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:03.897649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
salaried 38
76.0%
self-employed 7
 
14.0%
retired 3
 
6.0%
unemployed 2
 
4.0%

Most occurring characters

ValueCountFrequency (%)
a 76
17.4%
e 69
15.8%
l 54
12.4%
d 50
11.5%
S 45
10.3%
r 41
9.4%
i 41
9.4%
o 9
 
2.1%
y 9
 
2.1%
m 9
 
2.1%
Other values (7) 33
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 379
86.9%
Uppercase Letter 50
 
11.5%
Dash Punctuation 7
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 76
20.1%
e 69
18.2%
l 54
14.2%
d 50
13.2%
r 41
10.8%
i 41
10.8%
o 9
 
2.4%
y 9
 
2.4%
m 9
 
2.4%
p 9
 
2.4%
Other values (3) 12
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
S 45
90.0%
R 3
 
6.0%
U 2
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 429
98.4%
Common 7
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 76
17.7%
e 69
16.1%
l 54
12.6%
d 50
11.7%
S 45
10.5%
r 41
9.6%
i 41
9.6%
o 9
 
2.1%
y 9
 
2.1%
m 9
 
2.1%
Other values (6) 26
 
6.1%
Common
ValueCountFrequency (%)
- 7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 76
17.4%
e 69
15.8%
l 54
12.4%
d 50
11.5%
S 45
10.3%
r 41
9.4%
i 41
9.4%
o 9
 
2.1%
y 9
 
2.1%
m 9
 
2.1%
Other values (7) 33
7.6%

Job_Type
Categorical

Distinct6
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Sales
11 
Manager
11 
Engineer
10 
Teacher
Technician

Length

Max length10
Median length8
Mean length6.86
Min length5

Characters and Unicode

Total characters343
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowManager
3rd rowManager
4th rowEngineer
5th rowTechnician

Common Values

ValueCountFrequency (%)
Sales 11
22.0%
Manager 11
22.0%
Engineer 10
20.0%
Teacher 8
16.0%
Technician 5
10.0%
Clerk 5
10.0%

Length

2025-10-11T21:35:04.054258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:04.235386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
sales 11
22.0%
manager 11
22.0%
engineer 10
20.0%
teacher 8
16.0%
technician 5
10.0%
clerk 5
10.0%

Most occurring characters

ValueCountFrequency (%)
e 68
19.8%
a 46
13.4%
n 41
12.0%
r 34
9.9%
g 21
 
6.1%
i 20
 
5.8%
c 18
 
5.2%
l 16
 
4.7%
T 13
 
3.8%
h 13
 
3.8%
Other values (6) 53
15.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 293
85.4%
Uppercase Letter 50
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 68
23.2%
a 46
15.7%
n 41
14.0%
r 34
11.6%
g 21
 
7.2%
i 20
 
6.8%
c 18
 
6.1%
l 16
 
5.5%
h 13
 
4.4%
s 11
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
T 13
26.0%
S 11
22.0%
M 11
22.0%
E 10
20.0%
C 5
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 343
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 68
19.8%
a 46
13.4%
n 41
12.0%
r 34
9.9%
g 21
 
6.1%
i 20
 
5.8%
c 18
 
5.2%
l 16
 
4.7%
T 13
 
3.8%
h 13
 
3.8%
Other values (6) 53
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 68
19.8%
a 46
13.4%
n 41
12.0%
r 34
9.9%
g 21
 
6.1%
i 20
 
5.8%
c 18
 
5.2%
l 16
 
4.7%
T 13
 
3.8%
h 13
 
3.8%
Other values (6) 53
15.5%

Monthly_Income
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156267.66
Minimum35237
Maximum282764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:04.427511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum35237
5-th percentile42610.8
Q183768.5
median166646
Q3221101.5
95-th percentile263836.3
Maximum282764
Range247527
Interquartile range (IQR)137333

Descriptive statistics

Standard deviation76860.582
Coefficient of variation (CV)0.49185213
Kurtosis-1.3301587
Mean156267.66
Median Absolute Deviation (MAD)64560.5
Skewness-0.12117557
Sum7813383
Variance5.9075491 × 109
MonotonicityNot monotonic
2025-10-11T21:35:04.605601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130235 1
 
2.0%
226489 1
 
2.0%
256507 1
 
2.0%
269833 1
 
2.0%
282377 1
 
2.0%
196319 1
 
2.0%
151172 1
 
2.0%
89101 1
 
2.0%
53049 1
 
2.0%
240677 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
35237 1
2.0%
39435 1
2.0%
39540 1
2.0%
46364 1
2.0%
46964 1
2.0%
50056 1
2.0%
53049 1
2.0%
53289 1
2.0%
57663 1
2.0%
59375 1
2.0%
ValueCountFrequency (%)
282764 1
2.0%
282377 1
2.0%
269833 1
2.0%
256507 1
2.0%
255913 1
2.0%
249295 1
2.0%
246139 1
2.0%
240677 1
2.0%
233861 1
2.0%
226489 1
2.0%

Experience_Years
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.02
Minimum0
Maximum34
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:04.768321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.45
Q111
median22
Q328.75
95-th percentile34
Maximum34
Range34
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation10.284702
Coefficient of variation (CV)0.5137214
Kurtosis-1.1305363
Mean20.02
Median Absolute Deviation (MAD)10
Skewness-0.27432292
Sum1001
Variance105.7751
MonotonicityNot monotonic
2025-10-11T21:35:04.925400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
34 5
 
10.0%
11 4
 
8.0%
22 3
 
6.0%
32 3
 
6.0%
9 3
 
6.0%
28 3
 
6.0%
25 3
 
6.0%
23 3
 
6.0%
19 2
 
4.0%
31 2
 
4.0%
Other values (16) 19
38.0%
ValueCountFrequency (%)
0 1
 
2.0%
1 1
 
2.0%
2 1
 
2.0%
3 1
 
2.0%
6 1
 
2.0%
7 1
 
2.0%
8 1
 
2.0%
9 3
6.0%
10 1
 
2.0%
11 4
8.0%
ValueCountFrequency (%)
34 5
10.0%
33 1
 
2.0%
32 3
6.0%
31 2
 
4.0%
30 1
 
2.0%
29 1
 
2.0%
28 3
6.0%
25 3
6.0%
24 2
 
4.0%
23 3
6.0%

Loan_Purpose
Categorical

Distinct5
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
Personal
12 
Business
10 
Furniture
10 
Car
Education

Length

Max length9
Median length8
Mean length7.48
Min length3

Characters and Unicode

Total characters374
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCar
2nd rowPersonal
3rd rowBusiness
4th rowBusiness
5th rowCar

Common Values

ValueCountFrequency (%)
Personal 12
24.0%
Business 10
20.0%
Furniture 10
20.0%
Car 9
18.0%
Education 9
18.0%

Length

2025-10-11T21:35:05.110532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:05.348181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
personal 12
24.0%
business 10
20.0%
furniture 10
20.0%
car 9
18.0%
education 9
18.0%

Most occurring characters

ValueCountFrequency (%)
s 42
11.2%
r 41
11.0%
n 41
11.0%
u 39
10.4%
e 32
8.6%
a 30
8.0%
i 29
7.8%
o 21
 
5.6%
t 19
 
5.1%
P 12
 
3.2%
Other values (7) 68
18.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 324
86.6%
Uppercase Letter 50
 
13.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 42
13.0%
r 41
12.7%
n 41
12.7%
u 39
12.0%
e 32
9.9%
a 30
9.3%
i 29
9.0%
o 21
6.5%
t 19
5.9%
l 12
 
3.7%
Other values (2) 18
5.6%
Uppercase Letter
ValueCountFrequency (%)
P 12
24.0%
B 10
20.0%
F 10
20.0%
C 9
18.0%
E 9
18.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 374
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 42
11.2%
r 41
11.0%
n 41
11.0%
u 39
10.4%
e 32
8.6%
a 30
8.0%
i 29
7.8%
o 21
 
5.6%
t 19
 
5.1%
P 12
 
3.2%
Other values (7) 68
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 374
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 42
11.2%
r 41
11.0%
n 41
11.0%
u 39
10.4%
e 32
8.6%
a 30
8.0%
i 29
7.8%
o 21
 
5.6%
t 19
 
5.1%
P 12
 
3.2%
Other values (7) 68
18.2%

Monthly_Spending
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82568.76
Minimum18709
Maximum176426
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:05.566336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum18709
5-th percentile28823.2
Q141833
median67376
Q3126382
95-th percentile170295.5
Maximum176426
Range157717
Interquartile range (IQR)84549

Descriptive statistics

Standard deviation48550.158
Coefficient of variation (CV)0.58799669
Kurtosis-1.089647
Mean82568.76
Median Absolute Deviation (MAD)33561.5
Skewness0.54519506
Sum4128438
Variance2.3571178 × 109
MonotonicityNot monotonic
2025-10-11T21:35:05.767110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34065 1
 
2.0%
49620 1
 
2.0%
76629 1
 
2.0%
81040 1
 
2.0%
43016 1
 
2.0%
38960 1
 
2.0%
48591 1
 
2.0%
118333 1
 
2.0%
129821 1
 
2.0%
51395 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
18709 1
2.0%
19748 1
2.0%
28807 1
2.0%
28843 1
2.0%
30485 1
2.0%
32640 1
2.0%
34065 1
2.0%
34870 1
2.0%
35559 1
2.0%
36732 1
2.0%
ValueCountFrequency (%)
176426 1
2.0%
172504 1
2.0%
171542 1
2.0%
168772 1
2.0%
159188 1
2.0%
155507 1
2.0%
147396 1
2.0%
144695 1
2.0%
143148 1
2.0%
138961 1
2.0%

Savings_Balance
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean492118.04
Minimum14337
Maximum975240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:06.315024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum14337
5-th percentile78691.45
Q1270936.25
median473111.5
Q3723910.75
95-th percentile931741.55
Maximum975240
Range960903
Interquartile range (IQR)452974.5

Descriptive statistics

Standard deviation294786.75
Coefficient of variation (CV)0.59901635
Kurtosis-1.2385199
Mean492118.04
Median Absolute Deviation (MAD)245484
Skewness0.12536771
Sum24605902
Variance8.6899229 × 1010
MonotonicityNot monotonic
2025-10-11T21:35:06.501146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
264214 1
 
2.0%
897048 1
 
2.0%
635075 1
 
2.0%
692281 1
 
2.0%
300451 1
 
2.0%
425571 1
 
2.0%
132016 1
 
2.0%
796267 1
 
2.0%
549447 1
 
2.0%
502562 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
14337 1
2.0%
16023 1
2.0%
71203 1
2.0%
87844 1
2.0%
123012 1
2.0%
127402 1
2.0%
132016 1
2.0%
155159 1
2.0%
160576 1
2.0%
165775 1
2.0%
ValueCountFrequency (%)
975240 1
2.0%
960592 1
2.0%
933479 1
2.0%
929618 1
2.0%
910588 1
2.0%
897048 1
2.0%
891089 1
2.0%
888439 1
2.0%
870291 1
2.0%
870184 1
2.0%

Credit_Amount
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean984200.68
Minimum50404
Maximum1931548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:06.687254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum50404
5-th percentile172902.3
Q1517168.75
median971510.5
Q31406338.2
95-th percentile1899829.6
Maximum1931548
Range1881144
Interquartile range (IQR)889169.5

Descriptive statistics

Standard deviation573825.57
Coefficient of variation (CV)0.58303716
Kurtosis-1.134225
Mean984200.68
Median Absolute Deviation (MAD)451867
Skewness0.12928493
Sum49210034
Variance3.2927578 × 1011
MonotonicityNot monotonic
2025-10-11T21:35:06.902736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448929 1
 
2.0%
753286 1
 
2.0%
1433194 1
 
2.0%
1054208 1
 
2.0%
1931548 1
 
2.0%
201456 1
 
2.0%
1903025 1
 
2.0%
912312 1
 
2.0%
1204146 1
 
2.0%
320846 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
50404 1
2.0%
77712 1
2.0%
162816 1
2.0%
185230 1
2.0%
201456 1
2.0%
250244 1
2.0%
285365 1
2.0%
319114 1
2.0%
320846 1
2.0%
335977 1
2.0%
ValueCountFrequency (%)
1931548 1
2.0%
1908532 1
2.0%
1903025 1
2.0%
1895924 1
2.0%
1865790 1
2.0%
1816616 1
2.0%
1772320 1
2.0%
1763796 1
2.0%
1723361 1
2.0%
1685156 1
2.0%

Installment_Rate
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.2472
Minimum5.78
Maximum39.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:07.108530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5.78
5-th percentile6.946
Q114.8225
median24.29
Q331.8075
95-th percentile36.753
Maximum39.32
Range33.54
Interquartile range (IQR)16.985

Descriptive statistics

Standard deviation9.6831628
Coefficient of variation (CV)0.41653028
Kurtosis-1.0729652
Mean23.2472
Median Absolute Deviation (MAD)8.47
Skewness-0.21331544
Sum1162.36
Variance93.763641
MonotonicityNot monotonic
2025-10-11T21:35:07.309208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.5 1
 
2.0%
15.43 1
 
2.0%
26.6 1
 
2.0%
39.32 1
 
2.0%
26.28 1
 
2.0%
27.28 1
 
2.0%
24.42 1
 
2.0%
8.19 1
 
2.0%
30.42 1
 
2.0%
24.16 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
5.78 1
2.0%
6.61 1
2.0%
6.64 1
2.0%
7.32 1
2.0%
8.09 1
2.0%
8.19 1
2.0%
12.18 1
2.0%
12.25 1
2.0%
12.32 1
2.0%
13.44 1
2.0%
ValueCountFrequency (%)
39.32 1
2.0%
38.88 1
2.0%
36.87 1
2.0%
36.61 1
2.0%
35.74 1
2.0%
34.47 1
2.0%
34.1 1
2.0%
33.57 1
2.0%
33.18 1
2.0%
32.88 1
2.0%
Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Medium
28 
Low
13 
High

Length

Max length6
Median length6
Mean length4.86
Min length3

Characters and Unicode

Total characters243
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowMedium
4th rowMedium
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium 28
56.0%
Low 13
26.0%
High 9
 
18.0%

Length

2025-10-11T21:35:07.509340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:07.684451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
medium 28
56.0%
low 13
26.0%
high 9
 
18.0%

Most occurring characters

ValueCountFrequency (%)
i 37
15.2%
M 28
11.5%
e 28
11.5%
d 28
11.5%
u 28
11.5%
m 28
11.5%
L 13
 
5.3%
o 13
 
5.3%
w 13
 
5.3%
H 9
 
3.7%
Other values (2) 18
7.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 193
79.4%
Uppercase Letter 50
 
20.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 37
19.2%
e 28
14.5%
d 28
14.5%
u 28
14.5%
m 28
14.5%
o 13
 
6.7%
w 13
 
6.7%
g 9
 
4.7%
h 9
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
M 28
56.0%
L 13
26.0%
H 9
 
18.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 243
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 37
15.2%
M 28
11.5%
e 28
11.5%
d 28
11.5%
u 28
11.5%
m 28
11.5%
L 13
 
5.3%
o 13
 
5.3%
w 13
 
5.3%
H 9
 
3.7%
Other values (2) 18
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 37
15.2%
M 28
11.5%
e 28
11.5%
d 28
11.5%
u 28
11.5%
m 28
11.5%
L 13
 
5.3%
o 13
 
5.3%
w 13
 
5.3%
H 9
 
3.7%
Other values (2) 18
7.4%

Payment_History
Categorical

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Good
32 
Average
14 
Poor

Length

Max length7
Median length4
Mean length4.84
Min length4

Characters and Unicode

Total characters242
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowGood
3rd rowGood
4th rowAverage
5th rowGood

Common Values

ValueCountFrequency (%)
Good 32
64.0%
Average 14
28.0%
Poor 4
 
8.0%

Length

2025-10-11T21:35:07.822518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:07.993587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
good 32
64.0%
average 14
28.0%
poor 4
 
8.0%

Most occurring characters

ValueCountFrequency (%)
o 72
29.8%
G 32
13.2%
d 32
13.2%
e 28
 
11.6%
r 18
 
7.4%
A 14
 
5.8%
v 14
 
5.8%
a 14
 
5.8%
g 14
 
5.8%
P 4
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 192
79.3%
Uppercase Letter 50
 
20.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 72
37.5%
d 32
16.7%
e 28
 
14.6%
r 18
 
9.4%
v 14
 
7.3%
a 14
 
7.3%
g 14
 
7.3%
Uppercase Letter
ValueCountFrequency (%)
G 32
64.0%
A 14
28.0%
P 4
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 242
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 72
29.8%
G 32
13.2%
d 32
13.2%
e 28
 
11.6%
r 18
 
7.4%
A 14
 
5.8%
v 14
 
5.8%
a 14
 
5.8%
g 14
 
5.8%
P 4
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 242
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 72
29.8%
G 32
13.2%
d 32
13.2%
e 28
 
11.6%
r 18
 
7.4%
A 14
 
5.8%
v 14
 
5.8%
a 14
 
5.8%
g 14
 
5.8%
P 4
 
1.7%
Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size178.0 B
False
37 
True
13 
ValueCountFrequency (%)
False 37
74.0%
True 13
 
26.0%
2025-10-11T21:35:08.129784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Default_History
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size178.0 B
False
46 
True
 
4
ValueCountFrequency (%)
False 46
92.0%
True 4
 
8.0%
2025-10-11T21:35:08.235843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct4
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
<50K
25 
50K–100K
12 
>100K
No account

Length

Max length10
Median length9
Mean length5.62
Min length4

Characters and Unicode

Total characters281
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<50K
2nd row<50K
3rd row<50K
4th row<50K
5th row50K–100K

Common Values

ValueCountFrequency (%)
<50K 25
50.0%
50K–100K 12
24.0%
>100K 9
 
18.0%
No account 4
 
8.0%

Length

2025-10-11T21:35:08.370138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:08.556579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
50k 25
46.3%
50k–100k 12
22.2%
100k 9
 
16.7%
no 4
 
7.4%
account 4
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 79
28.1%
K 58
20.6%
5 37
13.2%
< 25
 
8.9%
1 21
 
7.5%
– 12
 
4.3%
> 9
 
3.2%
o 8
 
2.8%
c 8
 
2.8%
N 4
 
1.4%
Other values (5) 20
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 137
48.8%
Uppercase Letter 62
22.1%
Math Symbol 34
 
12.1%
Lowercase Letter 32
 
11.4%
Dash Punctuation 12
 
4.3%
Space Separator 4
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 8
25.0%
c 8
25.0%
a 4
12.5%
u 4
12.5%
n 4
12.5%
t 4
12.5%
Decimal Number
ValueCountFrequency (%)
0 79
57.7%
5 37
27.0%
1 21
 
15.3%
Uppercase Letter
ValueCountFrequency (%)
K 58
93.5%
N 4
 
6.5%
Math Symbol
ValueCountFrequency (%)
< 25
73.5%
> 9
 
26.5%
Dash Punctuation
ValueCountFrequency (%)
– 12
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 187
66.5%
Latin 94
33.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 58
61.7%
o 8
 
8.5%
c 8
 
8.5%
N 4
 
4.3%
a 4
 
4.3%
u 4
 
4.3%
n 4
 
4.3%
t 4
 
4.3%
Common
ValueCountFrequency (%)
0 79
42.2%
5 37
19.8%
< 25
 
13.4%
1 21
 
11.2%
– 12
 
6.4%
> 9
 
4.8%
4
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 269
95.7%
Punctuation 12
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 79
29.4%
K 58
21.6%
5 37
13.8%
< 25
 
9.3%
1 21
 
7.8%
> 9
 
3.3%
o 8
 
3.0%
c 8
 
3.0%
N 4
 
1.5%
4
 
1.5%
Other values (4) 16
 
5.9%
Punctuation
ValueCountFrequency (%)
– 12
100.0%
Distinct4
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
<50K
23 
50K–200K
17 
>200K
None
 
2

Length

Max length8
Median length6.5
Mean length5.52
Min length4

Characters and Unicode

Total characters276
Distinct characters11
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd row<50K
3rd row50K–200K
4th row<50K
5th row<50K

Common Values

ValueCountFrequency (%)
<50K 23
46.0%
50K–200K 17
34.0%
>200K 8
 
16.0%
None 2
 
4.0%

Length

2025-10-11T21:35:08.709779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:08.880163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
50k 23
46.0%
50k–200k 17
34.0%
200k 8
 
16.0%
none 2
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 90
32.6%
K 65
23.6%
5 40
14.5%
2 25
 
9.1%
< 23
 
8.3%
– 17
 
6.2%
> 8
 
2.9%
N 2
 
0.7%
o 2
 
0.7%
n 2
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 155
56.2%
Uppercase Letter 67
24.3%
Math Symbol 31
 
11.2%
Dash Punctuation 17
 
6.2%
Lowercase Letter 6
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 90
58.1%
5 40
25.8%
2 25
 
16.1%
Lowercase Letter
ValueCountFrequency (%)
o 2
33.3%
n 2
33.3%
e 2
33.3%
Uppercase Letter
ValueCountFrequency (%)
K 65
97.0%
N 2
 
3.0%
Math Symbol
ValueCountFrequency (%)
< 23
74.2%
> 8
 
25.8%
Dash Punctuation
ValueCountFrequency (%)
– 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 203
73.6%
Latin 73
 
26.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 90
44.3%
5 40
19.7%
2 25
 
12.3%
< 23
 
11.3%
– 17
 
8.4%
> 8
 
3.9%
Latin
ValueCountFrequency (%)
K 65
89.0%
N 2
 
2.7%
o 2
 
2.7%
n 2
 
2.7%
e 2
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 259
93.8%
Punctuation 17
 
6.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 90
34.7%
K 65
25.1%
5 40
15.4%
2 25
 
9.7%
< 23
 
8.9%
> 8
 
3.1%
N 2
 
0.8%
o 2
 
0.8%
n 2
 
0.8%
e 2
 
0.8%
Punctuation
ValueCountFrequency (%)
– 17
100.0%

Property
Categorical

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Real Estate
21 
None
15 
Car
14 

Length

Max length11
Median length4
Mean length6.66
Min length3

Characters and Unicode

Total characters333
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowReal Estate
3rd rowReal Estate
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
Real Estate 21
42.0%
None 15
30.0%
Car 14
28.0%

Length

2025-10-11T21:35:09.031705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:09.187810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
real 21
29.6%
estate 21
29.6%
none 15
21.1%
car 14
19.7%

Most occurring characters

ValueCountFrequency (%)
e 57
17.1%
a 56
16.8%
t 42
12.6%
R 21
 
6.3%
l 21
 
6.3%
21
 
6.3%
E 21
 
6.3%
s 21
 
6.3%
N 15
 
4.5%
o 15
 
4.5%
Other values (3) 43
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 241
72.4%
Uppercase Letter 71
 
21.3%
Space Separator 21
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 57
23.7%
a 56
23.2%
t 42
17.4%
l 21
 
8.7%
s 21
 
8.7%
o 15
 
6.2%
n 15
 
6.2%
r 14
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
R 21
29.6%
E 21
29.6%
N 15
21.1%
C 14
19.7%
Space Separator
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 312
93.7%
Common 21
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 57
18.3%
a 56
17.9%
t 42
13.5%
R 21
 
6.7%
l 21
 
6.7%
E 21
 
6.7%
s 21
 
6.7%
N 15
 
4.8%
o 15
 
4.8%
n 15
 
4.8%
Other values (2) 28
9.0%
Common
ValueCountFrequency (%)
21
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 333
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 57
17.1%
a 56
16.8%
t 42
12.6%
R 21
 
6.3%
l 21
 
6.3%
21
 
6.3%
E 21
 
6.3%
s 21
 
6.3%
N 15
 
4.5%
o 15
 
4.5%
Other values (3) 43
12.9%

Housing
Categorical

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Own
26 
Rent
13 
Family
11 

Length

Max length6
Median length3
Mean length3.92
Min length3

Characters and Unicode

Total characters196
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOwn
2nd rowFamily
3rd rowOwn
4th rowFamily
5th rowOwn

Common Values

ValueCountFrequency (%)
Own 26
52.0%
Rent 13
26.0%
Family 11
22.0%

Length

2025-10-11T21:35:09.321898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:09.467490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
own 26
52.0%
rent 13
26.0%
family 11
22.0%

Most occurring characters

ValueCountFrequency (%)
n 39
19.9%
O 26
13.3%
w 26
13.3%
R 13
 
6.6%
e 13
 
6.6%
t 13
 
6.6%
F 11
 
5.6%
a 11
 
5.6%
m 11
 
5.6%
i 11
 
5.6%
Other values (2) 22
11.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 146
74.5%
Uppercase Letter 50
 
25.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 39
26.7%
w 26
17.8%
e 13
 
8.9%
t 13
 
8.9%
a 11
 
7.5%
m 11
 
7.5%
i 11
 
7.5%
l 11
 
7.5%
y 11
 
7.5%
Uppercase Letter
ValueCountFrequency (%)
O 26
52.0%
R 13
26.0%
F 11
22.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 196
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 39
19.9%
O 26
13.3%
w 26
13.3%
R 13
 
6.6%
e 13
 
6.6%
t 13
 
6.6%
F 11
 
5.6%
a 11
 
5.6%
m 11
 
5.6%
i 11
 
5.6%
Other values (2) 22
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 39
19.9%
O 26
13.3%
w 26
13.3%
R 13
 
6.6%
e 13
 
6.6%
t 13
 
6.6%
F 11
 
5.6%
a 11
 
5.6%
m 11
 
5.6%
i 11
 
5.6%
Other values (2) 22
11.2%
Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size178.0 B
False
42 
True
ValueCountFrequency (%)
False 42
84.0%
True 8
 
16.0%
2025-10-11T21:35:09.595537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Account_Tenure_Months
Real number (ℝ)

Distinct42
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.96
Minimum9
Maximum232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:09.738596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile18.9
Q161.75
median134.5
Q3175.75
95-th percentile224.65
Maximum232
Range223
Interquartile range (IQR)114

Descriptive statistics

Standard deviation69.805257
Coefficient of variation (CV)0.55418591
Kurtosis-1.210664
Mean125.96
Median Absolute Deviation (MAD)55
Skewness-0.22832159
Sum6298
Variance4872.7739
MonotonicityNot monotonic
2025-10-11T21:35:09.905794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
232 2
 
4.0%
44 2
 
4.0%
175 2
 
4.0%
21 2
 
4.0%
146 2
 
4.0%
158 2
 
4.0%
113 2
 
4.0%
163 2
 
4.0%
126 1
 
2.0%
189 1
 
2.0%
Other values (32) 32
64.0%
ValueCountFrequency (%)
9 1
2.0%
15 1
2.0%
18 1
2.0%
20 1
2.0%
21 2
4.0%
23 1
2.0%
34 1
2.0%
39 1
2.0%
44 2
4.0%
51 1
2.0%
ValueCountFrequency (%)
232 2
4.0%
226 1
2.0%
223 1
2.0%
220 1
2.0%
219 1
2.0%
215 1
2.0%
199 1
2.0%
195 1
2.0%
190 1
2.0%
189 1
2.0%
Distinct42
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.34
Minimum7
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:10.080937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile15.45
Q141.5
median63
Q376.75
95-th percentile113.55
Maximum119
Range112
Interquartile range (IQR)35.25

Descriptive statistics

Standard deviation29.794267
Coefficient of variation (CV)0.4857233
Kurtosis-0.60699961
Mean61.34
Median Absolute Deviation (MAD)19
Skewness0.078946687
Sum3067
Variance887.69837
MonotonicityNot monotonic
2025-10-11T21:35:10.255086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
63 2
 
4.0%
53 2
 
4.0%
92 2
 
4.0%
67 2
 
4.0%
72 2
 
4.0%
66 2
 
4.0%
41 2
 
4.0%
29 2
 
4.0%
102 1
 
2.0%
43 1
 
2.0%
Other values (32) 32
64.0%
ValueCountFrequency (%)
7 1
2.0%
8 1
2.0%
15 1
2.0%
16 1
2.0%
18 1
2.0%
19 1
2.0%
22 1
2.0%
24 1
2.0%
29 2
4.0%
34 1
2.0%
ValueCountFrequency (%)
119 1
2.0%
118 1
2.0%
114 1
2.0%
113 1
2.0%
105 1
2.0%
102 1
2.0%
98 1
2.0%
97 1
2.0%
92 2
4.0%
83 1
2.0%

Avg_Transaction_Value
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27342.2
Minimum1726
Maximum48319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:10.434934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1726
5-th percentile8453.5
Q118390.5
median27051.5
Q337757.5
95-th percentile47301.25
Maximum48319
Range46593
Interquartile range (IQR)19367

Descriptive statistics

Standard deviation12813.73
Coefficient of variation (CV)0.46864299
Kurtosis-1.0381788
Mean27342.2
Median Absolute Deviation (MAD)9453.5
Skewness0.00083981259
Sum1367110
Variance1.6419169 × 108
MonotonicityNot monotonic
2025-10-11T21:35:10.648082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35157 1
 
2.0%
33284 1
 
2.0%
11222 1
 
2.0%
38443 1
 
2.0%
35701 1
 
2.0%
18764 1
 
2.0%
45641 1
 
2.0%
18674 1
 
2.0%
19024 1
 
2.0%
47007 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
1726 1
2.0%
8214 1
2.0%
8287 1
2.0%
8657 1
2.0%
8906 1
2.0%
9245 1
2.0%
11222 1
2.0%
12362 1
2.0%
12593 1
2.0%
15199 1
2.0%
ValueCountFrequency (%)
48319 1
2.0%
47600 1
2.0%
47542 1
2.0%
47007 1
2.0%
45641 1
2.0%
44642 1
2.0%
44513 1
2.0%
43874 1
2.0%
43520 1
2.0%
41059 1
2.0%
Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size178.0 B
True
36 
False
14 
ValueCountFrequency (%)
True 36
72.0%
False 14
 
28.0%
2025-10-11T21:35:10.839822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size178.0 B
True
25 
False
25 
ValueCountFrequency (%)
True 25
50.0%
False 25
50.0%
2025-10-11T21:35:10.972360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Number_Credit_Inquiries_6mo
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.52
Minimum0
Maximum7
Zeros7
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:11.084410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35.75
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation2.349468
Coefficient of variation (CV)0.66746251
Kurtosis-1.2308727
Mean3.52
Median Absolute Deviation (MAD)2
Skewness-0.0016050755
Sum176
Variance5.52
MonotonicityNot monotonic
2025-10-11T21:35:11.234832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 8
16.0%
0 7
14.0%
7 7
14.0%
2 6
12.0%
5 6
12.0%
6 6
12.0%
1 5
10.0%
4 5
10.0%
ValueCountFrequency (%)
0 7
14.0%
1 5
10.0%
2 6
12.0%
3 8
16.0%
4 5
10.0%
5 6
12.0%
6 6
12.0%
7 7
14.0%
ValueCountFrequency (%)
7 7
14.0%
6 6
12.0%
5 6
12.0%
4 5
10.0%
3 8
16.0%
2 6
12.0%
1 5
10.0%
0 7
14.0%

Credit_Limit
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1107963.2
Minimum101195
Maximum1968317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:11.424017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum101195
5-th percentile211868.4
Q1671006.25
median1096313
Q31630081.5
95-th percentile1932880.6
Maximum1968317
Range1867122
Interquartile range (IQR)959075.25

Descriptive statistics

Standard deviation564032
Coefficient of variation (CV)0.50907108
Kurtosis-1.1835642
Mean1107963.2
Median Absolute Deviation (MAD)446124.5
Skewness-0.030741042
Sum55398158
Variance3.181321 × 1011
MonotonicityNot monotonic
2025-10-11T21:35:11.845614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1651405 1
 
2.0%
342231 1
 
2.0%
1815982 1
 
2.0%
943093 1
 
2.0%
731503 1
 
2.0%
1104730 1
 
2.0%
727164 1
 
2.0%
1009192 1
 
2.0%
1162864 1
 
2.0%
101195 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
101195 1
2.0%
129083 1
2.0%
210081 1
2.0%
214053 1
2.0%
332725 1
2.0%
342231 1
2.0%
438590 1
2.0%
495485 1
2.0%
495864 1
2.0%
608819 1
2.0%
ValueCountFrequency (%)
1968317 1
2.0%
1950463 1
2.0%
1934276 1
2.0%
1931175 1
2.0%
1890117 1
2.0%
1854673 1
2.0%
1829240 1
2.0%
1815982 1
2.0%
1792495 1
2.0%
1783951 1
2.0%

Revolving_Balance
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237467.02
Minimum9078
Maximum488533
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:12.050763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum9078
5-th percentile40444.85
Q1125642
median232461.5
Q3329797
95-th percentile432939.1
Maximum488533
Range479455
Interquartile range (IQR)204155

Descriptive statistics

Standard deviation129089.82
Coefficient of variation (CV)0.54361155
Kurtosis-0.94051354
Mean237467.02
Median Absolute Deviation (MAD)102716
Skewness-0.0058135566
Sum11873351
Variance1.666418 × 1010
MonotonicityNot monotonic
2025-10-11T21:35:12.238377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242997 1
 
2.0%
319804 1
 
2.0%
338041 1
 
2.0%
212977 1
 
2.0%
127118 1
 
2.0%
330721 1
 
2.0%
196713 1
 
2.0%
9078 1
 
2.0%
418219 1
 
2.0%
324912 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
9078 1
2.0%
22431 1
2.0%
33320 1
2.0%
49153 1
2.0%
50861 1
2.0%
51047 1
2.0%
56985 1
2.0%
90827 1
2.0%
96347 1
2.0%
108737 1
2.0%
ValueCountFrequency (%)
488533 1
2.0%
477085 1
2.0%
443002 1
2.0%
420640 1
2.0%
418219 1
2.0%
395141 1
2.0%
392550 1
2.0%
374899 1
2.0%
374504 1
2.0%
338041 1
2.0%
Distinct5
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
4
12 
3
12 
5
11 
2
10 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row4
4th row3
5th row3

Common Values

ValueCountFrequency (%)
4 12
24.0%
3 12
24.0%
5 11
22.0%
2 10
20.0%
1 5
10.0%

Length

2025-10-11T21:35:12.403237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:12.554750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
4 12
24.0%
3 12
24.0%
5 11
22.0%
2 10
20.0%
1 5
10.0%

Most occurring characters

ValueCountFrequency (%)
4 12
24.0%
3 12
24.0%
5 11
22.0%
2 10
20.0%
1 5
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 12
24.0%
3 12
24.0%
5 11
22.0%
2 10
20.0%
1 5
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 12
24.0%
3 12
24.0%
5 11
22.0%
2 10
20.0%
1 5
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 12
24.0%
3 12
24.0%
5 11
22.0%
2 10
20.0%
1 5
10.0%
Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
None
20 
Basic
18 
Comprehensive
12 

Length

Max length13
Median length5
Mean length6.52
Min length4

Characters and Unicode

Total characters326
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowComprehensive
4th rowNone
5th rowComprehensive

Common Values

ValueCountFrequency (%)
None 20
40.0%
Basic 18
36.0%
Comprehensive 12
24.0%

Length

2025-10-11T21:35:12.707861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:12.849962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
none 20
40.0%
basic 18
36.0%
comprehensive 12
24.0%

Most occurring characters

ValueCountFrequency (%)
e 56
17.2%
o 32
9.8%
n 32
9.8%
s 30
9.2%
i 30
9.2%
N 20
 
6.1%
B 18
 
5.5%
a 18
 
5.5%
c 18
 
5.5%
C 12
 
3.7%
Other values (5) 60
18.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 276
84.7%
Uppercase Letter 50
 
15.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 56
20.3%
o 32
11.6%
n 32
11.6%
s 30
10.9%
i 30
10.9%
a 18
 
6.5%
c 18
 
6.5%
m 12
 
4.3%
p 12
 
4.3%
r 12
 
4.3%
Other values (2) 24
8.7%
Uppercase Letter
ValueCountFrequency (%)
N 20
40.0%
B 18
36.0%
C 12
24.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 326
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 56
17.2%
o 32
9.8%
n 32
9.8%
s 30
9.2%
i 30
9.2%
N 20
 
6.1%
B 18
 
5.5%
a 18
 
5.5%
c 18
 
5.5%
C 12
 
3.7%
Other values (5) 60
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 326
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 56
17.2%
o 32
9.8%
n 32
9.8%
s 30
9.2%
i 30
9.2%
N 20
 
6.1%
B 18
 
5.5%
a 18
 
5.5%
c 18
 
5.5%
C 12
 
3.7%
Other values (5) 60
18.4%
Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Medium
22 
High
14 
Low
14 

Length

Max length6
Median length4
Mean length4.6
Min length3

Characters and Unicode

Total characters230
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowMedium
4th rowHigh
5th rowLow

Common Values

ValueCountFrequency (%)
Medium 22
44.0%
High 14
28.0%
Low 14
28.0%

Length

2025-10-11T21:35:12.973521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:13.119640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
medium 22
44.0%
high 14
28.0%
low 14
28.0%

Most occurring characters

ValueCountFrequency (%)
i 36
15.7%
M 22
9.6%
e 22
9.6%
d 22
9.6%
u 22
9.6%
m 22
9.6%
H 14
 
6.1%
g 14
 
6.1%
h 14
 
6.1%
L 14
 
6.1%
Other values (2) 28
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 180
78.3%
Uppercase Letter 50
 
21.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 36
20.0%
e 22
12.2%
d 22
12.2%
u 22
12.2%
m 22
12.2%
g 14
 
7.8%
h 14
 
7.8%
o 14
 
7.8%
w 14
 
7.8%
Uppercase Letter
ValueCountFrequency (%)
M 22
44.0%
H 14
28.0%
L 14
28.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 230
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 36
15.7%
M 22
9.6%
e 22
9.6%
d 22
9.6%
u 22
9.6%
m 22
9.6%
H 14
 
6.1%
g 14
 
6.1%
h 14
 
6.1%
L 14
 
6.1%
Other values (2) 28
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 36
15.7%
M 22
9.6%
e 22
9.6%
d 22
9.6%
u 22
9.6%
m 22
9.6%
H 14
 
6.1%
g 14
 
6.1%
h 14
 
6.1%
L 14
 
6.1%
Other values (2) 28
12.2%

Region
Categorical

Distinct4
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
North
17 
South
14 
West
10 
East

Length

Max length5
Median length5
Mean length4.62
Min length4

Characters and Unicode

Total characters231
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth
2nd rowNorth
3rd rowEast
4th rowEast
5th rowSouth

Common Values

ValueCountFrequency (%)
North 17
34.0%
South 14
28.0%
West 10
20.0%
East 9
18.0%

Length

2025-10-11T21:35:13.250712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:13.397878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
north 17
34.0%
south 14
28.0%
west 10
20.0%
east 9
18.0%

Most occurring characters

ValueCountFrequency (%)
t 50
21.6%
o 31
13.4%
h 31
13.4%
s 19
 
8.2%
N 17
 
7.4%
r 17
 
7.4%
S 14
 
6.1%
u 14
 
6.1%
W 10
 
4.3%
e 10
 
4.3%
Other values (2) 18
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 181
78.4%
Uppercase Letter 50
 
21.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 50
27.6%
o 31
17.1%
h 31
17.1%
s 19
 
10.5%
r 17
 
9.4%
u 14
 
7.7%
e 10
 
5.5%
a 9
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
N 17
34.0%
S 14
28.0%
W 10
20.0%
E 9
18.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 231
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 50
21.6%
o 31
13.4%
h 31
13.4%
s 19
 
8.2%
N 17
 
7.4%
r 17
 
7.4%
S 14
 
6.1%
u 14
 
6.1%
W 10
 
4.3%
e 10
 
4.3%
Other values (2) 18
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 231
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 50
21.6%
o 31
13.4%
h 31
13.4%
s 19
 
8.2%
N 17
 
7.4%
r 17
 
7.4%
S 14
 
6.1%
u 14
 
6.1%
W 10
 
4.3%
e 10
 
4.3%
Other values (2) 18
 
7.8%
Distinct6
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
Finance
10 
Other
10 
IT
Manufacturing
Education

Length

Max length13
Median length9
Mean length7.36
Min length2

Characters and Unicode

Total characters368
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFinance
2nd rowIT
3rd rowHealthcare
4th rowManufacturing
5th rowEducation

Common Values

ValueCountFrequency (%)
Finance 10
20.0%
Other 10
20.0%
IT 9
18.0%
Manufacturing 9
18.0%
Education 7
14.0%
Healthcare 5
10.0%

Length

2025-10-11T21:35:13.551430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:13.737531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
finance 10
20.0%
other 10
20.0%
it 9
18.0%
manufacturing 9
18.0%
education 7
14.0%
healthcare 5
10.0%

Most occurring characters

ValueCountFrequency (%)
n 45
12.2%
a 45
12.2%
c 31
 
8.4%
t 31
 
8.4%
e 30
 
8.2%
i 26
 
7.1%
u 25
 
6.8%
r 24
 
6.5%
h 15
 
4.1%
F 10
 
2.7%
Other values (11) 86
23.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 309
84.0%
Uppercase Letter 59
 
16.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 45
14.6%
a 45
14.6%
c 31
10.0%
t 31
10.0%
e 30
9.7%
i 26
8.4%
u 25
8.1%
r 24
7.8%
h 15
 
4.9%
g 9
 
2.9%
Other values (4) 28
9.1%
Uppercase Letter
ValueCountFrequency (%)
F 10
16.9%
O 10
16.9%
I 9
15.3%
M 9
15.3%
T 9
15.3%
E 7
11.9%
H 5
8.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 368
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 45
12.2%
a 45
12.2%
c 31
 
8.4%
t 31
 
8.4%
e 30
 
8.2%
i 26
 
7.1%
u 25
 
6.8%
r 24
 
6.5%
h 15
 
4.1%
F 10
 
2.7%
Other values (11) 86
23.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 45
12.2%
a 45
12.2%
c 31
 
8.4%
t 31
 
8.4%
e 30
 
8.2%
i 26
 
7.1%
u 25
 
6.8%
r 24
 
6.5%
h 15
 
4.1%
F 10
 
2.7%
Other values (11) 86
23.4%

Days_Since_Last_Default
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0
41 
300
 
4
900
 
3
600
 
2

Length

Max length3
Median length1
Mean length1.36
Min length1

Characters and Unicode

Total characters68
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 41
82.0%
300 4
 
8.0%
900 3
 
6.0%
600 2
 
4.0%

Length

2025-10-11T21:35:13.897668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:14.069202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 41
82.0%
300 4
 
8.0%
900 3
 
6.0%
600 2
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 59
86.8%
3 4
 
5.9%
9 3
 
4.4%
6 2
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59
86.8%
3 4
 
5.9%
9 3
 
4.4%
6 2
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 68
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59
86.8%
3 4
 
5.9%
9 3
 
4.4%
6 2
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59
86.8%
3 4
 
5.9%
9 3
 
4.4%
6 2
 
2.9%

Spending_to_Income_Ratio
Real number (ℝ)

Distinct40
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8304
Minimum0.09
Maximum4.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:14.221260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile0.1545
Q10.2425
median0.45
Q30.835
95-th percentile2.7175
Maximum4.04
Range3.95
Interquartile range (IQR)0.5925

Descriptive statistics

Standard deviation0.92081588
Coefficient of variation (CV)1.1088823
Kurtosis3.1275227
Mean0.8304
Median Absolute Deviation (MAD)0.25
Skewness1.903049
Sum41.52
Variance0.84790188
MonotonicityNot monotonic
2025-10-11T21:35:14.401618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.24 3
 
6.0%
0.26 2
 
4.0%
0.71 2
 
4.0%
0.3 2
 
4.0%
0.19 2
 
4.0%
0.25 2
 
4.0%
0.41 2
 
4.0%
0.2 2
 
4.0%
0.27 2
 
4.0%
2.15 1
 
2.0%
Other values (30) 30
60.0%
ValueCountFrequency (%)
0.09 1
 
2.0%
0.13 1
 
2.0%
0.15 1
 
2.0%
0.16 1
 
2.0%
0.19 2
4.0%
0.2 2
4.0%
0.21 1
 
2.0%
0.22 1
 
2.0%
0.24 3
6.0%
0.25 2
4.0%
ValueCountFrequency (%)
4.04 1
2.0%
3.45 1
2.0%
2.74 1
2.0%
2.69 1
2.0%
2.45 1
2.0%
2.25 1
2.0%
2.15 1
2.0%
1.84 1
2.0%
1.47 1
2.0%
1.33 1
2.0%

Loan_to_Income_Ratio
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.873
Minimum0.35
Maximum42.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:14.600419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.35
5-th percentile0.9615
Q12.6925
median6
Q315.6725
95-th percentile28.4255
Maximum42.73
Range42.38
Interquartile range (IQR)12.98

Descriptive statistics

Standard deviation10.027892
Coefficient of variation (CV)1.0156885
Kurtosis1.1696714
Mean9.873
Median Absolute Deviation (MAD)3.645
Skewness1.3678597
Sum493.65
Variance100.55863
MonotonicityNot monotonic
2025-10-11T21:35:14.785087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.45 1
 
2.0%
3.33 1
 
2.0%
5.59 1
 
2.0%
3.91 1
 
2.0%
6.84 1
 
2.0%
1.03 1
 
2.0%
12.59 1
 
2.0%
10.24 1
 
2.0%
22.7 1
 
2.0%
1.33 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
0.35 1
2.0%
0.51 1
2.0%
0.93 1
2.0%
1 1
2.0%
1.03 1
2.0%
1.33 1
2.0%
1.64 1
2.0%
1.68 1
2.0%
1.8 1
2.0%
2.28 1
2.0%
ValueCountFrequency (%)
42.73 1
2.0%
30.59 1
2.0%
29.6 1
2.0%
26.99 1
2.0%
25.77 1
2.0%
24.53 1
2.0%
24.12 1
2.0%
23.87 1
2.0%
22.7 1
2.0%
20.16 1
2.0%

Credit_Utilization_Ratio
Real number (ℝ)

Distinct38
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41
Minimum0.01
Maximum3.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:14.954667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.03
Q10.12
median0.23
Q30.3375
95-th percentile1.557
Maximum3.78
Range3.77
Interquartile range (IQR)0.2175

Descriptive statistics

Standard deviation0.71405996
Coefficient of variation (CV)1.7416097
Kurtosis14.409127
Mean0.41
Median Absolute Deviation (MAD)0.11
Skewness3.7502588
Sum20.5
Variance0.50988163
MonotonicityNot monotonic
2025-10-11T21:35:15.119718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.12 4
 
8.0%
0.23 3
 
6.0%
0.15 2
 
4.0%
0.03 2
 
4.0%
0.27 2
 
4.0%
0.06 2
 
4.0%
0.29 2
 
4.0%
0.08 2
 
4.0%
0.17 2
 
4.0%
0.34 1
 
2.0%
Other values (28) 28
56.0%
ValueCountFrequency (%)
0.01 1
 
2.0%
0.02 1
 
2.0%
0.03 2
4.0%
0.05 1
 
2.0%
0.06 2
4.0%
0.08 2
4.0%
0.09 1
 
2.0%
0.11 1
 
2.0%
0.12 4
8.0%
0.13 1
 
2.0%
ValueCountFrequency (%)
3.78 1
2.0%
3.21 1
2.0%
2.07 1
2.0%
0.93 1
2.0%
0.74 1
2.0%
0.67 1
2.0%
0.61 1
2.0%
0.49 1
2.0%
0.42 1
2.0%
0.38 1
2.0%

Behavioral_Score
Real number (ℝ)

Distinct32
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.1
Minimum41
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-10-11T21:35:15.304279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile41
Q146.25
median61.5
Q375.75
95-th percentile97
Maximum99
Range58
Interquartile range (IQR)29.5

Descriptive statistics

Standard deviation18.227866
Coefficient of variation (CV)0.28887268
Kurtosis-0.86968099
Mean63.1
Median Absolute Deviation (MAD)15
Skewness0.48016272
Sum3155
Variance332.2551
MonotonicityNot monotonic
2025-10-11T21:35:15.456838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
41 4
 
8.0%
77 3
 
6.0%
47 3
 
6.0%
42 3
 
6.0%
97 2
 
4.0%
75 2
 
4.0%
71 2
 
4.0%
72 2
 
4.0%
43 2
 
4.0%
78 2
 
4.0%
Other values (22) 25
50.0%
ValueCountFrequency (%)
41 4
8.0%
42 3
6.0%
43 2
4.0%
44 1
 
2.0%
45 2
4.0%
46 1
 
2.0%
47 3
6.0%
48 1
 
2.0%
50 1
 
2.0%
52 2
4.0%
ValueCountFrequency (%)
99 1
 
2.0%
98 1
 
2.0%
97 2
4.0%
96 1
 
2.0%
92 1
 
2.0%
90 1
 
2.0%
78 2
4.0%
77 3
6.0%
76 1
 
2.0%
75 2
4.0%

Credit_Risk
Categorical

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Good
30 
Bad
20 

Length

Max length4
Median length4
Mean length3.6
Min length3

Characters and Unicode

Total characters180
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowGood
3rd rowGood
4th rowGood
5th rowGood

Common Values

ValueCountFrequency (%)
Good 30
60.0%
Bad 20
40.0%

Length

2025-10-11T21:35:15.629717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T21:35:15.783005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
good 30
60.0%
bad 20
40.0%

Most occurring characters

ValueCountFrequency (%)
o 60
33.3%
d 50
27.8%
G 30
16.7%
B 20
 
11.1%
a 20
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 130
72.2%
Uppercase Letter 50
 
27.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 60
46.2%
d 50
38.5%
a 20
 
15.4%
Uppercase Letter
ValueCountFrequency (%)
G 30
60.0%
B 20
40.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 180
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 60
33.3%
d 50
27.8%
G 30
16.7%
B 20
 
11.1%
a 20
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 60
33.3%
d 50
27.8%
G 30
16.7%
B 20
 
11.1%
a 20
 
11.1%

Interactions

2025-10-11T21:34:57.058394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:11.824275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:14.892205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:18.034126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:20.229447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:22.533764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:25.159435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:27.458852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:30.056831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:32.423141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:35.092766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:38.194358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:41.007051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:44.201071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:47.607593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:49.890980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:52.278968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:54.623023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:57.169588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:12.177544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:15.047263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:18.145728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:20.350080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:22.656366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:25.272658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:27.582486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:30.168437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:32.573348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:35.247614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:38.329644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:41.196151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:44.361918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:47.728829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:50.017616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:52.556281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:54.743250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:57.310839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:12.352192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:15.221993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:18.266919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:20.472599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:22.797557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:25.400260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:27.714684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:30.291671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:32.722604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:35.409250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:38.482836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:41.404398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:44.533490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:47.856074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:50.150260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:52.680478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:54.867490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:57.443485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:12.522250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:15.380535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:18.375554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:20.580149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:22.918235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:25.515017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:27.844880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:30.407306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:32.859797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:35.551494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:38.620440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:41.572509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:44.697746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:47.985714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:50.272181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:52.784711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:54.995122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:57.578789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:12.697852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:15.556239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:18.502200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:20.694766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:23.057429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:25.657209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:27.981482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:30.545473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:33.009401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:35.705142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:38.782371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:41.762863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:44.868998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:48.116317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:50.409782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:52.905313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:55.134726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:57.713382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:12.897571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:15.742911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:18.636445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:20.836559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:23.202073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:25.811913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:28.133674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:30.680085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:33.184643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:35.901967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:38.951613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:41.942413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:45.056202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:48.257217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:50.554459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:53.042945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:55.277639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:57.837559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:13.065605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2025-10-11T21:34:25.022244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:27.335252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:29.922558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:32.260464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:34.950130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:38.041162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:40.799132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:44.020983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:47.476989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:49.754785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:52.148112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:54.502412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-10-11T21:34:56.926749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2025-10-11T21:35:15.987623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
AgeResidence_YearsMonthly_IncomeExperience_YearsMonthly_SpendingSavings_BalanceCredit_AmountInstallment_RateAccount_Tenure_MonthsMonthly_Transactions_CountAvg_Transaction_ValueNumber_Credit_Inquiries_6moCredit_LimitRevolving_BalanceSpending_to_Income_RatioLoan_to_Income_RatioCredit_Utilization_RatioBehavioral_ScoreCustomer_IDGenderMarital_StatusDependentsEducation_LevelEmployment_StatusJob_TypeLoan_PurposeCredit_Card_UsagePayment_HistoryOther_LoansDefault_HistoryChecking_Account_StatusSavings_Account_StatusPropertyHousingForeign_WorkerMobile_Banking_ActiveAuto_Pay_EnabledNum_Products_With_BankInsurance_CoverageFinancial_Literacy_ScoreRegionEmployment_IndustryDays_Since_Last_DefaultCredit_Risk
Age1.000-0.063-0.084-0.018-0.035-0.037-0.0450.191-0.134-0.0180.025-0.1170.0210.271-0.0150.0190.1950.2571.0000.2270.0420.0000.0000.0000.0920.0000.0000.0000.0000.0000.0000.0000.1750.3460.0000.1760.2160.2090.0000.1820.0000.0000.0000.168
Residence_Years-0.0631.0000.0300.082-0.390-0.236-0.035-0.0690.1640.004-0.008-0.1470.106-0.140-0.317-0.103-0.126-0.0301.0000.2310.0000.0000.0000.0000.1460.1340.0000.3790.3600.0000.2420.3000.1910.0000.0000.0000.0940.0990.0000.1630.1890.0000.0000.000
Monthly_Income-0.0840.0301.000-0.200-0.113-0.030-0.1090.1160.2390.1380.0830.113-0.212-0.029-0.682-0.6070.0980.0971.0000.3440.0000.0000.0000.0000.0000.3150.0980.0000.0000.0760.0000.3150.0000.2950.3840.4120.0000.0000.2230.2150.0000.0000.0000.590
Experience_Years-0.0180.082-0.2001.000-0.142-0.150-0.127-0.1130.092-0.2240.1370.128-0.0790.065-0.010-0.0040.1690.0741.0000.3500.0000.0000.3220.1250.0000.1610.1620.0000.0000.3060.0000.0000.0000.0000.4100.0000.0000.0240.0000.0000.2490.2520.0000.000
Monthly_Spending-0.035-0.390-0.113-0.1421.0000.227-0.0800.232-0.1380.193-0.0900.008-0.237-0.3570.7530.018-0.154-0.0331.0000.3260.1090.0000.0610.2750.0000.1590.0740.3070.0000.0000.0000.1560.0000.0000.0000.0000.2320.1810.0000.0000.1280.2080.0000.219
Savings_Balance-0.037-0.236-0.030-0.1500.2271.000-0.062-0.1370.087-0.099-0.123-0.0920.120-0.1430.2090.004-0.2470.0271.0000.0000.1990.0000.1820.0000.1940.1710.1230.0000.2240.0000.0000.0000.0000.2980.0930.2270.2650.2580.0000.0000.2950.0990.0000.000
Credit_Amount-0.045-0.035-0.109-0.127-0.080-0.0621.000-0.0930.0490.013-0.091-0.0310.035-0.0730.0150.813-0.001-0.2171.0000.0000.0000.0000.0000.0000.0000.0000.3190.0000.0000.1950.0000.0000.2030.1410.3840.2260.0000.0690.1870.1470.1010.0000.0000.362
Installment_Rate0.191-0.0690.116-0.1130.232-0.137-0.0931.000-0.2020.282-0.0430.026-0.0740.1050.101-0.1460.1370.2741.0000.0000.0000.0000.0000.0000.2070.1500.0000.1070.1870.1560.1260.0810.3090.0000.3090.0000.0000.2130.0000.0000.0000.0000.0000.000
Account_Tenure_Months-0.1340.1640.2390.092-0.1380.0870.049-0.2021.000-0.1900.231-0.2450.0380.122-0.205-0.0840.0490.1111.0000.2900.0000.1180.0000.0000.0460.1820.2310.1910.2840.0000.0000.1410.2660.0000.1580.3330.0000.0000.0000.0000.0000.0960.0720.000
Monthly_Transactions_Count-0.0180.0040.138-0.2240.193-0.0990.0130.282-0.1901.0000.1340.056-0.0590.0000.036-0.0490.0260.0671.0000.3860.5300.0000.2540.0000.1430.0000.0000.1950.0000.0000.0000.1010.0000.2090.2460.1700.2860.2080.1000.0880.1790.0000.1310.000
Avg_Transaction_Value0.025-0.0080.0830.137-0.090-0.123-0.091-0.0430.2310.1341.000-0.104-0.1180.084-0.083-0.1140.079-0.1551.0000.0000.1910.0000.1650.0000.0000.0000.0000.0000.0000.0000.0000.0000.1720.3460.0340.0000.0700.1870.0900.0000.2120.1330.0000.000
Number_Credit_Inquiries_6mo-0.117-0.1470.1130.1280.008-0.092-0.0310.026-0.2450.056-0.1041.000-0.111-0.120-0.041-0.0750.018-0.0961.0000.1130.0000.0000.0000.0940.0000.1090.0000.0690.2220.1900.0880.2240.0000.1680.0000.0000.0000.1930.0000.0000.0000.1030.0000.000
Credit_Limit0.0210.106-0.212-0.079-0.2370.1200.035-0.0740.038-0.059-0.118-0.1111.000-0.0200.0180.146-0.6070.0481.0000.5290.0000.0000.0000.0000.1050.1870.1470.3830.0000.0000.0000.0000.1720.0000.0000.1540.2000.0000.2020.0000.1320.2740.1360.249
Revolving_Balance0.271-0.140-0.0290.065-0.357-0.143-0.0730.1050.1220.0000.084-0.120-0.0201.000-0.2320.0330.7520.1761.0000.2640.0000.0000.1350.1450.1560.1400.2540.1030.0000.4240.3710.0000.2220.0000.0000.3350.0000.1490.0000.0000.3310.0490.0000.215
Spending_to_Income_Ratio-0.015-0.317-0.682-0.0100.7530.2090.0150.101-0.2050.036-0.083-0.0410.018-0.2321.0000.411-0.215-0.1161.0000.0880.0000.2360.1610.1360.0000.0000.3430.0000.0000.3990.0000.2130.0000.2190.0000.1170.0000.2130.2560.0000.0640.0000.0510.402
Loan_to_Income_Ratio0.019-0.103-0.607-0.0040.0180.0040.813-0.146-0.084-0.049-0.114-0.0750.1460.0330.4111.000-0.011-0.2421.0000.0000.4230.0000.0000.0000.2810.1690.0560.0000.0000.0000.0000.0250.0580.0890.0000.2770.1900.1180.3550.0000.2630.0900.0000.669
Credit_Utilization_Ratio0.195-0.1260.0980.169-0.154-0.247-0.0010.1370.0490.0260.0790.018-0.6070.752-0.215-0.0111.0000.1211.0000.0000.0000.1320.0000.0000.1310.0900.0000.0000.0000.0000.0430.0000.0000.2220.0000.0000.0000.0000.0860.2090.1030.0000.0000.000
Behavioral_Score0.257-0.0300.0970.074-0.0330.027-0.2170.2740.1110.067-0.155-0.0960.0480.176-0.116-0.2420.1211.0001.0000.0000.0000.1660.1250.3410.0830.0000.2570.0000.0000.0000.2840.0000.0000.2760.0000.3840.2750.0000.0000.0000.0000.0000.2290.292
Customer_ID1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Gender0.2270.2310.3440.3500.3260.0000.0000.0000.2900.3860.0000.1130.5290.2640.0880.0000.0000.0001.0001.0000.1380.2230.0000.2350.0000.1120.0730.0000.0000.0000.0000.3660.0000.0000.0000.0000.0000.0000.0330.0000.2640.0000.0000.000
Marital_Status0.0420.0000.0000.0000.1090.1990.0000.0000.0000.5300.1910.0000.0000.0000.0000.4230.0000.0001.0000.1381.0000.0000.1930.0000.1720.0000.0640.0000.0000.2040.1770.0000.0000.1840.3070.0000.0650.0000.0000.0000.0450.1530.0000.000
Dependents0.0000.0000.0000.0000.0000.0000.0000.0000.1180.0000.0000.0000.0000.0000.2360.0000.1320.1661.0000.2230.0001.0000.1580.1390.0570.0000.1840.0000.0000.0000.0000.0000.0000.0000.1610.0000.0000.0000.1100.0720.1140.2520.0000.000
Education_Level0.0000.0000.0000.3220.0610.1820.0000.0000.0000.2540.1650.0000.0000.1350.1610.0000.0000.1251.0000.0000.1930.1581.0000.1240.0000.0000.0000.1660.0000.2980.1240.1760.0000.0000.1010.0000.3490.0000.0650.0000.2340.0000.1940.000
Employment_Status0.0000.0000.0000.1250.2750.0000.0000.0000.0000.0000.0000.0940.0000.1450.1360.0000.0000.3411.0000.2350.0000.1390.1241.0000.0000.0000.0000.0650.0000.2310.1300.2760.0000.0900.0000.0000.0000.0000.2320.0000.1820.0000.2390.000
Job_Type0.0920.1460.0000.0000.0000.1940.0000.2070.0460.1430.0000.0000.1050.1560.0000.2810.1310.0831.0000.0000.1720.0570.0000.0001.0000.0000.0000.0000.1500.0000.0920.0550.3010.2540.1510.0000.2070.1240.0000.0000.0000.1680.0870.112
Loan_Purpose0.0000.1340.3150.1610.1590.1710.0000.1500.1820.0000.0000.1090.1870.1400.0000.1690.0900.0001.0000.1120.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0140.1050.0000.0000.0000.0000.2420.0000.1820.0000.1880.313
Credit_Card_Usage0.0000.0000.0980.1620.0740.1230.3190.0000.2310.0000.0000.0000.1470.2540.3430.0560.0000.2571.0000.0730.0640.1840.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.1380.0000.3330.0000.0000.0730.1040.0660.0000.1420.1760.000
Payment_History0.0000.3790.0000.0000.3070.0000.0000.1070.1910.1950.0000.0690.3830.1030.0000.0000.0000.0001.0000.0000.0000.0000.1660.0650.0000.0000.0001.0000.0000.1300.2830.0000.0000.0920.0000.2140.1580.2260.1280.0000.0000.0000.3130.303
Other_Loans0.0000.3600.0000.0000.0000.2240.0000.1870.2840.0000.0000.2220.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.1500.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1660.0000.0000.0000.0000.000
Default_History0.0000.0000.0760.3060.0000.0000.1950.1560.0000.0000.0000.1900.0000.4240.3990.0000.0000.0001.0000.0000.2040.0000.2980.2310.0000.0000.0000.1300.0001.0000.0790.2280.0910.3430.0000.0000.0000.0600.0000.0000.0000.1170.0460.250
Checking_Account_Status0.0000.2420.0000.0000.0000.0000.0000.1260.0000.0000.0000.0880.0000.3710.0000.0000.0430.2841.0000.0000.1770.0000.1240.1300.0920.0000.0000.2830.0000.0791.0000.1990.2640.0000.0000.2610.0000.2260.0000.0750.0000.0000.1430.147
Savings_Account_Status0.0000.3000.3150.0000.1560.0000.0000.0810.1410.1010.0000.2240.0000.0000.2130.0250.0000.0001.0000.3660.0000.0000.1760.2760.0550.0000.0000.0000.0000.2280.1991.0000.0000.0000.0000.0000.0000.0000.0000.0000.0920.1990.0000.000
Property0.1750.1910.0000.0000.0000.0000.2030.3090.2660.0000.1720.0000.1720.2220.0000.0580.0000.0001.0000.0000.0000.0000.0000.0000.3010.0140.1380.0000.0000.0910.2640.0001.0000.3410.2870.0830.0000.0000.0000.0730.2430.0000.1100.297
Housing0.3460.0000.2950.0000.0000.2980.1410.0000.0000.2090.3460.1680.0000.0000.2190.0890.2220.2761.0000.0000.1840.0000.0000.0900.2540.1050.0000.0920.0000.3430.0000.0000.3411.0000.0000.0000.0000.1390.0000.0690.0760.2240.0000.193
Foreign_Worker0.0000.0000.3840.4100.0000.0930.3840.3090.1580.2460.0340.0000.0000.0000.0000.0000.0000.0001.0000.0000.3070.1610.1010.0000.1510.0000.3330.0000.0000.0000.0000.0000.2870.0001.0000.0560.0000.0000.0000.3330.0000.0650.0000.000
Mobile_Banking_Active0.1760.0000.4120.0000.0000.2270.2260.0000.3330.1700.0000.0000.1540.3350.1170.2770.0000.3841.0000.0000.0000.0000.0000.0000.0000.0000.0000.2140.0000.0000.2610.0000.0830.0000.0561.0000.0000.0000.1330.0440.2050.3330.0000.098
Auto_Pay_Enabled0.2160.0940.0000.0000.2320.2650.0000.0000.0000.2860.0700.0000.2000.0000.0000.1900.0000.2751.0000.0000.0650.0000.3490.0000.2070.0000.0000.1580.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.1490.0000.0000.0000.1000.147
Num_Products_With_Bank0.2090.0990.0000.0240.1810.2580.0690.2130.0000.2080.1870.1930.0000.1490.2130.1180.0000.0001.0000.0000.0000.0000.0000.0000.1240.0000.0730.2260.0000.0600.2260.0000.0000.1390.0000.0000.0001.0000.0000.0000.2280.0000.0000.000
Insurance_Coverage0.0000.0000.2230.0000.0000.0000.1870.0000.0000.1000.0900.0000.2020.0000.2560.3550.0860.0001.0000.0330.0000.1100.0650.2320.0000.2420.1040.1280.1660.0000.0000.0000.0000.0000.0000.1330.1490.0001.0000.0000.1390.0000.0970.358
Financial_Literacy_Score0.1820.1630.2150.0000.0000.0000.1470.0000.0000.0880.0000.0000.0000.0000.0000.0000.2090.0001.0000.0000.0000.0720.0000.0000.0000.0000.0660.0000.0000.0000.0750.0000.0730.0690.3330.0440.0000.0000.0001.0000.0000.0000.0000.000
Region0.0000.1890.0000.2490.1280.2950.1010.0000.0000.1790.2120.0000.1320.3310.0640.2630.1030.0001.0000.2640.0450.1140.2340.1820.0000.1820.0000.0000.0000.0000.0000.0920.2430.0760.0000.2050.0000.2280.1390.0001.0000.0000.0000.000
Employment_Industry0.0000.0000.0000.2520.2080.0990.0000.0000.0960.0000.1330.1030.2740.0490.0000.0900.0000.0001.0000.0000.1530.2520.0000.0000.1680.0000.1420.0000.0000.1170.0000.1990.0000.2240.0650.3330.0000.0000.0000.0000.0001.0000.2150.000
Days_Since_Last_Default0.0000.0000.0000.0000.0000.0000.0000.0000.0720.1310.0000.0000.1360.0000.0510.0000.0000.2291.0000.0000.0000.0000.1940.2390.0870.1880.1760.3130.0000.0460.1430.0000.1100.0000.0000.0000.1000.0000.0970.0000.0000.2151.0000.000
Credit_Risk0.1680.0000.5900.0000.2190.0000.3620.0000.0000.0000.0000.0000.2490.2150.4020.6690.0000.2921.0000.0000.0000.0000.0000.0000.1120.3130.0000.3030.0000.2500.1470.0000.2970.1930.0000.0980.1470.0000.3580.0000.0000.0000.0001.000

Missing values

2025-10-11T21:35:00.103895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-11T21:35:01.002793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Customer_IDAgeGenderMarital_StatusDependentsResidence_YearsEducation_LevelEmployment_StatusJob_TypeMonthly_IncomeExperience_YearsLoan_PurposeMonthly_SpendingSavings_BalanceCredit_AmountInstallment_RateCredit_Card_UsagePayment_HistoryOther_LoansDefault_HistoryChecking_Account_StatusSavings_Account_StatusPropertyHousingForeign_WorkerAccount_Tenure_MonthsMonthly_Transactions_CountAvg_Transaction_ValueMobile_Banking_ActiveAuto_Pay_EnabledNumber_Credit_Inquiries_6moCredit_LimitRevolving_BalanceNum_Products_With_BankInsurance_CoverageFinancial_Literacy_ScoreRegionEmployment_IndustryDays_Since_Last_DefaultSpending_to_Income_RatioLoan_to_Income_RatioCredit_Utilization_RatioBehavioral_ScoreCredit_Risk
0C00159MaleSingle014High SchoolSalariedSales13023519Car3406526421444892931.50MediumGoodNoNo<50KNoneNoneOwnNo1769835157YesYes216514052429972NoneMediumSouthFinance00.263.450.1597Good
1C00249FemaleMarried42GraduateSalariedManager10474023Personal10309284407063046618.22MediumGoodNoNo<50K<50KReal EstateFamilyNo1735312593YesYes01290834885334NoneMediumNorthIT00.986.023.7896Good
2C00335MaleSingle35GraduateSelf-employedManager25591311Business6969397524062369513.44MediumGoodNoNo<50K50K–200KReal EstateOwnNo223741042YesNo1608819963474ComprehensiveMediumEastHealthcare00.272.440.1652Good
3C00463FemaleMarried312High SchoolSalariedEngineer20724734Business4196250610851355612.18MediumAverageYesNo<50K<50KNoneFamilyYes1884918320YesYes313694323109313NoneHighEastManufacturing00.202.480.2364Good
4C00528MaleSingle310High SchoolSalariedTechnician16887732Car4329563942162527813.80MediumGoodNoNo50K–100K<50KNoneOwnYes2201828376NoYes514648212976323ComprehensiveLowSouthEducation00.263.700.2041Good
5C00641FemaleWidowed36PhDSalariedClerk3523732Furniture28807373501104317614.62MediumGoodNoNo>100K<50KReal EstateRentYes1133444513YesNo619504633365854BasicLowEastOther00.8229.600.1774Bad
6C00759MaleMarried37GraduateSelf-employedManager5005611Furniture172504729226122804912.25LowAverageYesNo<50K50K–200KNoneOwnNo4411815199NoNo116976082057354BasicLowSouthOther03.4524.530.1246Bad
7C00839MaleSingle24High SchoolUnemployedEngineer2066152Personal147396155159103304935.74MediumGoodNoNo<50K>200KNoneOwnYes207216876NoYes4827147224312BasicHighSouthHealthcare00.715.000.0360Good
8C00943FemaleSingle17PhDRetiredTeacher2176280Car10626717750252800731.49MediumGoodNoNo<50K<50KNoneOwnNo125229245YesNo210983823238252BasicMediumSouthOther00.492.430.2957Good
9C01031MaleMarried311PhDSalariedEngineer14454832Business348701605768945736.64HighGoodNoNo50K–100K50K–200KCarRentNo9668214NoNo77040371945553ComprehensiveMediumSouthIT3000.246.190.2842Good
Customer_IDAgeGenderMarital_StatusDependentsResidence_YearsEducation_LevelEmployment_StatusJob_TypeMonthly_IncomeExperience_YearsLoan_PurposeMonthly_SpendingSavings_BalanceCredit_AmountInstallment_RateCredit_Card_UsagePayment_HistoryOther_LoansDefault_HistoryChecking_Account_StatusSavings_Account_StatusPropertyHousingForeign_WorkerAccount_Tenure_MonthsMonthly_Transactions_CountAvg_Transaction_ValueMobile_Banking_ActiveAuto_Pay_EnabledNumber_Credit_Inquiries_6moCredit_LimitRevolving_BalanceNum_Products_With_BankInsurance_CoverageFinancial_Literacy_ScoreRegionEmployment_IndustryDays_Since_Last_DefaultSpending_to_Income_RatioLoan_to_Income_RatioCredit_Utilization_RatioBehavioral_ScoreCredit_Risk
40C04159FemaleDivorced01GraduateSalariedClerk3954022Education10839632242876546032.88MediumPoorNoNo>100K>200KReal EstateRentNo215624265YesYes16435863951415BasicMediumNorthOther02.7419.360.6147Bad
41C04238MaleSingle28GraduateSalariedTechnician4636431Education3673232418793457421.08MediumAverageNoNo<50K>200KReal EstateFamilyYes189826931YesNo219311752150485NoneHighNorthOther00.7920.160.1142Bad
42C04324FemaleMarried07GraduateSalariedSales5766311Furniture106362695907176379634.47LowGoodNoNo<50K<50KReal EstateFamilyNo1266329841YesNo318292402362931ComprehensiveMediumNorthManufacturing01.8430.590.1342Bad
43C04445MaleMarried12High SchoolUnemployedManager8199112Car17642688843928536531.91LowPoorNoYes>100K50K–200KReal EstateFamilyNo157630192YesYes515352001178324BasicMediumNorthManufacturing9002.153.480.0861Bad
44C04534FemaleSingle18GraduateSalariedManager21875122Personal28843878443927867.32LowGoodNoNo50K–100K<50KCarRentNo1632447542YesNo611380753925501NoneHighWestOther00.131.800.3445Good
45C04629MaleSingle31PhDSalariedTeacher19204924Personal12906555682011491886.61MediumPoorYesNoNo account<50KReal EstateOwnNo1589743520YesNo014865361251505NoneLowNorthFinance3000.675.980.0841Bad
46C04746MaleSingle411GraduateSalariedManager12470434Education8672617042186000626.73HighGoodYesNo>100K50K–200KReal EstateOwnNo1086716585YesNo54954851570872NoneMediumNorthIT00.706.900.3262Good
47C04822FemaleSingle012PostgraduateSalariedTeacher7911529Car32640127402190853217.16MediumAverageNoNo<50K50K–200KCarOwnNo895826559NoNo51792495333205BasicMediumNorthEducation00.4124.120.0241Bad
48C04940FemaleDivorced09GraduateSalariedEngineer24929516Education6752812301225024412.32LowAverageNoNo50K–100K<50KNoneOwnNo13811941059YesNo613437743270254NoneHighNorthManufacturing00.271.000.2477Good
49C05048MaleMarried29GraduateSelf-employedClerk22196219Business53413443661189592425.29HighAverageNoNo<50K50K–200KReal EstateOwnNo7011334742YesYes01566111908275ComprehensiveLowEastManufacturing9000.248.540.0656Good